An Efficient Density-based Approach for Data Mining Tasks

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An Efficient Density-based Approach for Data Mining Tasks

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by Carlotta Domeniconi , Dimitrios Gunopulos

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BibTeX

@MISC{Domeniconi_anefficient,
    author = {Carlotta Domeniconi and Dimitrios Gunopulos},
    title = {An Efficient Density-based Approach for Data Mining Tasks},
    year = {}
}

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Abstract

Abstract. We propose a locally adaptive technique to address the problem of setting the bandwidth parameters for kernel density estimation. Our technique is efficient and can be performed in only two dataset passes. We also show how to apply our technique to efficiently solve range query approximation, classification and clustering problems for very large datasets. We validate the efficiency and accuracy of our technique by presenting experimental results on a variety of both synthetic and real datasets.

Keyphrases

efficient density-based approach data mining task clustering problem range query approximation adaptive technique dataset pass real datasets bandwidth parameter large datasets kernel density estimation experimental result

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